{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:48.994867Z",
     "iopub.status.busy": "2021-08-12T02:44:48.994341Z",
     "iopub.status.idle": "2021-08-12T02:44:49.241860Z",
     "shell.execute_reply": "2021-08-12T02:44:49.241120Z",
     "shell.execute_reply.started": "2021-08-12T02:44:48.994819Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:51.832398Z",
     "iopub.status.busy": "2021-08-12T02:44:51.831794Z",
     "iopub.status.idle": "2021-08-12T02:44:51.955912Z",
     "shell.execute_reply": "2021-08-12T02:44:51.954451Z",
     "shell.execute_reply.started": "2021-08-12T02:44:51.832347Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用量 611464\n",
      "-rw-rw-r-- 1 lyz lyz     49324 8月   5 16:54 sample_submit.csv\n",
      "-rw-rw-r-- 1 lyz lyz 151068800 7月  31 18:11 test.npy\n",
      "-rw-rw-r-- 1 lyz lyz  30636709 8月   5 16:57 test.npy.zip\n",
      "-rw-rw-r-- 1 lyz lyz    577557 8月   5 16:54 train.csv\n",
      "-rw-rw-r-- 1 lyz lyz 368640128 7月  31 18:11 train.npy\n",
      "-rw-rw-r-- 1 lyz lyz  75144987 8月   5 16:57 train.npy.zip\n"
     ]
    }
   ],
   "source": [
    "!ls ./人脸关键点检测挑战赛_数据集/ -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:52.511356Z",
     "iopub.status.busy": "2021-08-12T02:44:52.510790Z",
     "iopub.status.idle": "2021-08-12T02:44:52.541184Z",
     "shell.execute_reply": "2021-08-12T02:44:52.540507Z",
     "shell.execute_reply.started": "2021-08-12T02:44:52.511307Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>left_eye_center_x</th>\n",
       "      <th>left_eye_center_y</th>\n",
       "      <th>right_eye_center_x</th>\n",
       "      <th>right_eye_center_y</th>\n",
       "      <th>nose_tip_x</th>\n",
       "      <th>nose_tip_y</th>\n",
       "      <th>mouth_center_bottom_lip_x</th>\n",
       "      <th>mouth_center_bottom_lip_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66.342364</td>\n",
       "      <td>38.523613</td>\n",
       "      <td>28.930840</td>\n",
       "      <td>35.577773</td>\n",
       "      <td>49.256845</td>\n",
       "      <td>68.275955</td>\n",
       "      <td>47.783946</td>\n",
       "      <td>85.361582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.912604</td>\n",
       "      <td>31.409117</td>\n",
       "      <td>29.652226</td>\n",
       "      <td>33.028075</td>\n",
       "      <td>51.913358</td>\n",
       "      <td>48.408453</td>\n",
       "      <td>50.698868</td>\n",
       "      <td>79.574038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.708994</td>\n",
       "      <td>40.371149</td>\n",
       "      <td>27.130820</td>\n",
       "      <td>40.940680</td>\n",
       "      <td>44.502523</td>\n",
       "      <td>69.988486</td>\n",
       "      <td>45.926427</td>\n",
       "      <td>86.221009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.334176</td>\n",
       "      <td>35.471878</td>\n",
       "      <td>29.366461</td>\n",
       "      <td>37.767684</td>\n",
       "      <td>50.411373</td>\n",
       "      <td>64.934767</td>\n",
       "      <td>50.028780</td>\n",
       "      <td>74.883241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.634857</td>\n",
       "      <td>29.999486</td>\n",
       "      <td>31.094571</td>\n",
       "      <td>29.616429</td>\n",
       "      <td>50.247429</td>\n",
       "      <td>51.450857</td>\n",
       "      <td>47.948571</td>\n",
       "      <td>84.394286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   left_eye_center_x  left_eye_center_y  right_eye_center_x  \\\n",
       "0          66.342364          38.523613           28.930840   \n",
       "1          68.912604          31.409117           29.652226   \n",
       "2          68.708994          40.371149           27.130820   \n",
       "3          65.334176          35.471878           29.366461   \n",
       "4          68.634857          29.999486           31.094571   \n",
       "\n",
       "   right_eye_center_y  nose_tip_x  nose_tip_y  mouth_center_bottom_lip_x  \\\n",
       "0           35.577773   49.256845   68.275955                  47.783946   \n",
       "1           33.028075   51.913358   48.408453                  50.698868   \n",
       "2           40.940680   44.502523   69.988486                  45.926427   \n",
       "3           37.767684   50.411373   64.934767                  50.028780   \n",
       "4           29.616429   50.247429   51.450857                  47.948571   \n",
       "\n",
       "   mouth_center_bottom_lip_y  \n",
       "0                  85.361582  \n",
       "1                  79.574038  \n",
       "2                  86.221009  \n",
       "3                  74.883241  \n",
       "4                  84.394286  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('人脸关键点检测挑战赛_数据集/train.csv')\n",
    "train_df = train_df.fillna(48)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:54.982695Z",
     "iopub.status.busy": "2021-08-12T02:44:54.982109Z",
     "iopub.status.idle": "2021-08-12T02:44:55.142852Z",
     "shell.execute_reply": "2021-08-12T02:44:55.142345Z",
     "shell.execute_reply.started": "2021-08-12T02:44:54.982645Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96, 96, 5000) (96, 96, 2049)\n"
     ]
    }
   ],
   "source": [
    "train_img = np.load('人脸关键点检测挑战赛_数据集/train.npy')\n",
    "test_img = np.load('人脸关键点检测挑战赛_数据集/test.npy')\n",
    "\n",
    "print(train_img.shape, test_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:55.896562Z",
     "iopub.status.busy": "2021-08-12T02:44:55.896044Z",
     "iopub.status.idle": "2021-08-12T02:44:56.171677Z",
     "shell.execute_reply": "2021-08-12T02:44:56.171193Z",
     "shell.execute_reply.started": "2021-08-12T02:44:55.896523Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4fc3353240>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "idx = 409\n",
    "xy = train_df.iloc[idx].values.reshape(-1, 2)\n",
    "plt.scatter(xy[:, 0], xy[:, 1], c='r')\n",
    "plt.imshow(train_img[:, :, idx], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:56.811765Z",
     "iopub.status.busy": "2021-08-12T02:44:56.811207Z",
     "iopub.status.idle": "2021-08-12T02:44:56.913564Z",
     "shell.execute_reply": "2021-08-12T02:44:56.913090Z",
     "shell.execute_reply.started": "2021-08-12T02:44:56.811720Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4fe5202240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "idx = 400\n",
    "xy = train_df.iloc[idx].values.reshape(-1, 2)\n",
    "plt.scatter(xy[:, 0], xy[:, 1])\n",
    "plt.imshow(train_img[:, :, idx], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:57.071662Z",
     "iopub.status.busy": "2021-08-12T02:44:57.071118Z",
     "iopub.status.idle": "2021-08-12T02:44:57.546290Z",
     "shell.execute_reply": "2021-08-12T02:44:57.545674Z",
     "shell.execute_reply.started": "2021-08-12T02:44:57.071616Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, sys, codecs, glob\n",
    "from PIL import Image, ImageDraw\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "\n",
    "import torch\n",
    "torch.backends.cudnn.benchmark = False\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as datasets\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data.dataset import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:57.547418Z",
     "iopub.status.busy": "2021-08-12T02:44:57.547167Z",
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     "shell.execute_reply": "2021-08-12T02:44:57.551336Z",
     "shell.execute_reply.started": "2021-08-12T02:44:57.547402Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class XunFeiDataset(Dataset):\n",
    "    def __init__(self, img, keypoint, transform=None):\n",
    "        self.img = img\n",
    "        self.transform = transform\n",
    "        self.keypoint = keypoint\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = Image.fromarray(self.img[:, :, index]).convert('RGB')\n",
    "        \n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        \n",
    "        return img, self.keypoint[index] / 96.0\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.img.shape[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:57.576892Z",
     "iopub.status.busy": "2021-08-12T02:44:57.576396Z",
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     "shell.execute_reply": "2021-08-12T02:44:57.616811Z",
     "shell.execute_reply.started": "2021-08-12T02:44:57.576849Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "    XunFeiDataset(train_img[:, :, :-500], train_df.values[:-500],\n",
    "                    transforms.Compose([\n",
    "                        transforms.ToTensor(),\n",
    "        ])\n",
    "    ),\n",
    "    batch_size=10, shuffle=True, num_workers=5,\n",
    ")\n",
    "\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "    XunFeiDataset(train_img[:, :, -500:], train_df.values[-500:],\n",
    "                    transforms.Compose([\n",
    "                        transforms.ToTensor(),\n",
    "        ])\n",
    "    ),\n",
    "    batch_size=10, shuffle=False, num_workers=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:57.891061Z",
     "iopub.status.busy": "2021-08-12T02:44:57.890534Z",
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     "shell.execute_reply": "2021-08-12T02:44:57.896435Z",
     "shell.execute_reply.started": "2021-08-12T02:44:57.891016Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 96, 500)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_img[:, :, -500:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:44:58.628550Z",
     "iopub.status.busy": "2021-08-12T02:44:58.627967Z",
     "iopub.status.idle": "2021-08-12T02:45:00.715425Z",
     "shell.execute_reply": "2021-08-12T02:45:00.714551Z",
     "shell.execute_reply.started": "2021-08-12T02:44:58.628505Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import timm\n",
    "\n",
    "class XunFeiNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(XunFeiNet, self).__init__()\n",
    "        self.model = timm.create_model('efficientnet_b2', num_classes=8, \n",
    "                          pretrained=True)\n",
    "        \n",
    "    def forward(self, img, labels=None):        \n",
    "        feat = self.model(img)\n",
    "        return feat\n",
    "    \n",
    "model = XunFeiNet().cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:45:00.716461Z",
     "iopub.status.busy": "2021-08-12T02:45:00.716244Z",
     "iopub.status.idle": "2021-08-12T02:45:00.721741Z",
     "shell.execute_reply": "2021-08-12T02:45:00.721294Z",
     "shell.execute_reply.started": "2021-08-12T02:45:00.716443Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train(train_loader, model, criterion, optimizer, epoch):\n",
    "    model.train()\n",
    "\n",
    "    for i, (input, target) in enumerate(train_loader):\n",
    "        input = input.cuda(non_blocking=True).float()\n",
    "        target = target.cuda(non_blocking=True).float()\n",
    "\n",
    "        output = model(input, target)\n",
    "        loss = criterion(output, target)\n",
    "        optimizer.zero_grad()\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if i % 200 == 0:\n",
    "            print(loss.item())\n",
    "            \n",
    "def validate(val_loader, model):\n",
    "    model.eval()\n",
    "    \n",
    "    val_feats = []\n",
    "    with torch.no_grad():\n",
    "        end = time.time()\n",
    "        for i, (input, target) in enumerate(val_loader):\n",
    "            input = input.cuda().float()\n",
    "            target = target.cuda().float()\n",
    "            output = model(input)\n",
    "            val_feats.append(output.data.cpu().numpy())\n",
    "    return val_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:45:01.973773Z",
     "iopub.status.busy": "2021-08-12T02:45:01.973192Z",
     "iopub.status.idle": "2021-08-12T02:47:39.099452Z",
     "shell.execute_reply": "2021-08-12T02:47:39.098728Z",
     "shell.execute_reply.started": "2021-08-12T02:45:01.973726Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0\n",
      "11.421347618103027\n",
      "0.03704027459025383\n",
      "0.014666966162621975\n",
      "Val 9.279472900988633\n",
      "Epoch:  1\n",
      "0.012085829861462116\n",
      "0.006038994062691927\n",
      "0.0032732977997511625\n",
      "Val 5.567091234611738\n",
      "Epoch:  2\n",
      "0.005394747946411371\n",
      "0.003243999322876334\n",
      "0.005800812970846891\n",
      "Val 4.703693337560008\n",
      "Epoch:  3\n",
      "0.003982057794928551\n",
      "0.005444754846394062\n",
      "0.0016011203406378627\n",
      "Val 3.85483954339147\n",
      "Epoch:  4\n",
      "0.001417716033756733\n",
      "0.00257496046833694\n",
      "0.001298340386711061\n",
      "Val 3.4021918732378675\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "criterion = nn.MSELoss().cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(), 0.001)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.85)\n",
    "best_acc = 0.0\n",
    "\n",
    "for epoch in range(5):\n",
    "    print('Epoch: ', epoch)\n",
    "\n",
    "    train(train_loader, model, criterion, optimizer, epoch)\n",
    "    \n",
    "    val_feats = validate(val_loader, model)\n",
    "    scheduler.step()\n",
    "    \n",
    "    val_feats = np.vstack(val_feats) * 96\n",
    "    print('Val', mean_absolute_error(val_feats, train_df.values[-500:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:49:44.797559Z",
     "iopub.status.busy": "2021-08-12T02:49:44.796935Z",
     "iopub.status.idle": "2021-08-12T02:49:44.803531Z",
     "shell.execute_reply": "2021-08-12T02:49:44.802855Z",
     "shell.execute_reply.started": "2021-08-12T02:49:44.797506Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_loader = torch.utils.data.DataLoader(\n",
    "    XunFeiDataset(test_img, np.zeros((2049, 8)),\n",
    "                    transforms.Compose([\n",
    "                        transforms.ToTensor(),\n",
    "        ])\n",
    "    ),\n",
    "    batch_size=10, shuffle=False, num_workers=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:49:45.892869Z",
     "iopub.status.busy": "2021-08-12T02:49:45.892307Z",
     "iopub.status.idle": "2021-08-12T02:49:48.530598Z",
     "shell.execute_reply": "2021-08-12T02:49:48.530126Z",
     "shell.execute_reply.started": "2021-08-12T02:49:45.892823Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: UserWarning: This overload of cuda is deprecated:\n",
      "\tcuda(torch.device device, bool async, *, torch.memory_format memory_format)\n",
      "Consider using one of the following signatures instead:\n",
      "\tcuda(torch.device device, bool non_blocking, *, torch.memory_format memory_format) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:882.)\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "pred_tta = []\n",
    "for tti in range(1):\n",
    "    pred = []\n",
    "    with torch.no_grad():\n",
    "        for t, (x, y) in enumerate(test_loader):\n",
    "            x_var = Variable(x).cuda(async=True)\n",
    "            y_var = Variable(y).cuda()\n",
    "            scores = model(x_var)\n",
    "            pred.append(scores.data.cpu().numpy())\n",
    "    pred = np.concatenate(pred, 0)    \n",
    "    print(tti)\n",
    "    pred_tta.append(pred)\n",
    "\n",
    "pred = np.mean(pred_tta, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-12T02:49:54.656910Z",
     "iopub.status.busy": "2021-08-12T02:49:54.656406Z",
     "iopub.status.idle": "2021-08-12T02:49:54.705064Z",
     "shell.execute_reply": "2021-08-12T02:49:54.704350Z",
     "shell.execute_reply.started": "2021-08-12T02:49:54.656880Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "col = ['left_eye_center_x', 'left_eye_center_y', 'right_eye_center_x',\n",
    " 'right_eye_center_y', 'nose_tip_x', 'nose_tip_y', \n",
    " 'mouth_center_bottom_lip_x','mouth_center_bottom_lip_y']\n",
    "pd.DataFrame(pred * 96, columns=col).to_csv('submit.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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